Month-to-month all-cause mortality forecasting: A method to rapidly detect changes in seasonal patterns

Background Short-term forecasts of all-cause mortality are used retrospectively to estimate the baseline mortality and to obtain excess death after mortality shocks, such as heatwaves and pandemics, have occurred. In this study we propose a flexible method to forecast all-cause mortality in real-time and to rapidly identify short-term changes in all-cause mortality seasonal patterns within an epidemiological year. Methods We use all-cause monthly death counts and ratios of death counts between adjacent months as inputs. The ratio between one month (earlier month) and the consecutive month (later month) is called later/earlier ratio. We forecast the deaths one-month-ahead based on their proportion to the previous month, defined by the average later/earlier ratio over the preceding years. We provide forecasting intervals by way of a bootstrapping procedure. Results The method is applied to monthly mortality data for Denmark, France, Spain, and Sweden from 2012 through 2022. Over the epidemiological years before COVID-19, the method captures the variations in winter and summer mortality peaks. The results reflect the synchrony of COVID-19 waves and the corresponding mortality burdens in the four analyzed countries. The forecasts show a higher level of accuracy compared to traditional models for short-term forecasting, i.e., 5-year-average method and Serfling model. Conclusion The method proposed is attractive for health researchers and governmental offices to aid public health responses, because it uses minimal input data, i.e., monthly all-cause mortality data, which are timely available and comparable across countries. Keymessages ### Competing Interest Statement The authors have declared no competing interest. ### Funding Statement AL was supported by the AXA Research Fund AXA Chair in Longevity Research and SR by Rockwool Foundation Excess Death Grant. ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines and uploaded the relevant EQUATOR Network research reporting checklist(s) and other pertinent material as supplementary files, if applicable. Yes Data from national statistical offices were used in this study. The full dataset and documentation can be downloaded from Statistics Denmark, INSEE, INE, and Statistics Sweden. The websites are provided in the Supplementary Materials. [https://www.statistikdatabasen.scb.se/pxweb/en/ssd/START\_\_BE\_\_BE0101__BE0101I/DodaManadReg/][1] [1]: https://www.statistikdatabasen.scb.se/pxweb/en/ssd/START__BE__BE0101__BE0101I/DodaManadReg/